Channel-Prioritized Convolutional Neural Networks for Sparsity and Multi-fidelity
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Updated
Feb 23, 2018 - Python
Channel-Prioritized Convolutional Neural Networks for Sparsity and Multi-fidelity
Improved Implementation of Single Shot MultiBox Detector, RefineDet and Network Optimization in Pytorch 07/2018
Efficient Sparse-Winograd Convolutional Neural Networks (ICLR 2018)
SNIP: SINGLE-SHOT NETWORK PRUNING BASED ON CONNECTION SENSITIVITY
Tensorflow codes for "Rethinking the Smaller-Norm-Less-Informative Assumption in Channel Pruning of Convolution Layers"
Code for "EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis" https://arxiv.org/abs/1905.05934
Implementation of Autoslim using Tensorflow2
Rethinking the Value of Network Pruning (Pytorch) (ICLR 2019)
[ICLR 2020]: 'AtomNAS: Fine-Grained End-to-End Neural Architecture Search'
CAE-ADMM: Implicit Bitrate Optimization via ADMM-Based Pruning in Compressive Autoencoders
Reducing the computational overhead of Deep CNNs through parameter pruning and tensor decomposition.
Lookahead: A Far-sighted Alternative of Magnitude-based Pruning (ICLR 2020)
Cheng-Hao Tu, Jia-Hong Lee, Yi-Ming Chan and Chu-Song Chen, "Pruning Depthwise Separable Convolutions for MobileNet Compression," International Joint Conference on Neural Networks, IJCNN 2020, July 2020.
This repository contains a Pytorch implementation of the article "The Lottery Ticket Hypothesis: Finding Sparse, Trainable Neural Networks" and an application of this hypothesis to reinforcement learning
Code for "Co-Evolutionary Compression for Unpaired Image Translation" (ICCV 2019), "SCOP: Scientific Control for Reliable Neural Network Pruning" (NeurIPS 2020) and “Manifold Regularized Dynamic Network Pruning” (CVPR 2021).
Pruning neural networks directly with back-propagation
Sparse variational droput in tensorflow2
[ICCV 2017] Learning Efficient Convolutional Networks through Network Slimming
[NIPS 2016] Learning Structured Sparsity in Deep Neural Networks
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